reports/report.ipynb

   1 {
   2  "cells": [
   3   {
   4    "cell_type": "markdown",
   5    "metadata": {},
   6    "source": [
   7     "\n",
   8     "\n",
   9     "<h2>Introduction</h2>\n",
  10     "\n",
  11     "J'ai fait quelques essais dans différentes configurations pour la méthode \"Neighbors\"\n",
  12     "(la seule dont on a parlé).<br>Il semble que le mieux soit\n",
  13     "\n",
  14     " * simtype=\"exo\" ou \"mix\" : similarités exogènes avec/sans endogènes (fenêtre optimisée par VC)\n",
  15     " * same_season=FALSE : les indices pour la validation croisée ne tiennent pas compte des saisons\n",
  16     " * mix_strategy=\"mult\" : on multiplie les poids (au lieu d'en éteindre)\n",
  17     "\n",
  18     "J'ai systématiquement comparé à une approche naïve : la moyennes des lendemains des jours\n",
  19     "\"similaires\" dans tout le passé ; à chaque fois sans prédiction du saut (sauf pour Neighbors :\n",
  20     "prédiction basée sur les poids calculés).\n",
  21     "\n",
  22     "Ensuite j'affiche les erreurs, quelques courbes prévues/mesurées, quelques filaments puis les\n",
  23     "histogrammes de quelques poids. Concernant les graphes de filaments, la moitié gauche du graphe\n",
  24     "correspond aux jours similaires au jour courant, tandis que la moitié droite affiche les\n",
  25     "lendemains : ce sont donc les voisinages tels qu'utilisés dans l'algorithme.\n",
  26     "\n"
  27    ]
  28   },
  29   {
  30    "cell_type": "code",
  31    "execution_count": null,
  32    "metadata": {
  33     "collapsed": false
  34    },
  35    "outputs": [],
  36    "source": [
  37     "library(talweg)\n",
  38     "\n",
  39     "ts_data = read.csv(system.file(\"extdata\",\"pm10_mesures_H_loc_report.csv\",package=\"talweg\"))\n",
  40     "exo_data = read.csv(system.file(\"extdata\",\"meteo_extra_noNAs.csv\",package=\"talweg\"))\n",
  41     "data = getData(ts_data, exo_data, input_tz = \"Europe/Paris\", working_tz=\"Europe/Paris\", predict_at=7)\n",
  42     "\n",
  43     "indices_ch = seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\")\n",
  44     "indices_ep = seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\")\n",
  45     "indices_np = seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\")\n",
  46     "\n",
  47     "H = 3 #predict from 2pm to 4pm"
  48    ]
  49   },
  50   {
  51    "cell_type": "markdown",
  52    "metadata": {},
  53    "source": [
  54     "\n",
  55     "\n",
  56     "<h2 style=\"color:blue;font-size:2em\">Pollution par chauffage</h2>"
  57    ]
  58   },
  59   {
  60    "cell_type": "code",
  61    "execution_count": null,
  62    "metadata": {
  63     "collapsed": false
  64    },
  65    "outputs": [],
  66    "source": [
  67     "p_nn_exo = computeForecast(data, indices_ch, \"Neighbors\", \"Neighbors\", simtype=\"exo\", horizon=H)\n",
  68     "p_nn_mix = computeForecast(data, indices_ch, \"Neighbors\", \"Neighbors\", simtype=\"mix\", horizon=H)\n",
  69     "p_az = computeForecast(data, indices_ch, \"Average\", \"Zero\", horizon=H) #, memory=183)\n",
  70     "p_pz = computeForecast(data, indices_ch, \"Persistence\", \"Zero\", horizon=H, same_day=TRUE)"
  71    ]
  72   },
  73   {
  74    "cell_type": "code",
  75    "execution_count": null,
  76    "metadata": {
  77     "collapsed": false
  78    },
  79    "outputs": [],
  80    "source": [
  81     "e_nn_exo = computeError(data, p_nn_exo, H)\n",
  82     "e_nn_mix = computeError(data, p_nn_mix, H)\n",
  83     "e_az = computeError(data, p_az, H)\n",
  84     "e_pz = computeError(data, p_pz, H)\n",
  85     "\n",
  86     "options(repr.plot.width=9, repr.plot.height=7)\n",
  87     "plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4))\n",
  88     "\n",
  89     "# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence\n",
  90     "\n",
  91     "i_np = which.min(e_nn_exo$abs$indices)\n",
  92     "i_p = which.max(e_nn_exo$abs$indices)"
  93    ]
  94   },
  95   {
  96    "cell_type": "code",
  97    "execution_count": null,
  98    "metadata": {
  99     "collapsed": false
 100    },
 101    "outputs": [],
 102    "source": [
 103     "options(repr.plot.width=9, repr.plot.height=4)\n",
 104     "par(mfrow=c(1,2))\n",
 105     "\n",
 106     "plotPredReal(data, p_nn_exo, i_np); title(paste(\"PredReal nn exo day\",i_np))\n",
 107     "plotPredReal(data, p_nn_exo, i_p); title(paste(\"PredReal nn exo day\",i_p))\n",
 108     "\n",
 109     "plotPredReal(data, p_nn_mix, i_np); title(paste(\"PredReal nn mix day\",i_np))\n",
 110     "plotPredReal(data, p_nn_mix, i_p); title(paste(\"PredReal nn mix day\",i_p))\n",
 111     "\n",
 112     "plotPredReal(data, p_az, i_np); title(paste(\"PredReal az day\",i_np))\n",
 113     "plotPredReal(data, p_az, i_p); title(paste(\"PredReal az day\",i_p))\n",
 114     "\n",
 115     "# Bleu: prévue, noir: réalisée"
 116    ]
 117   },
 118   {
 119    "cell_type": "code",
 120    "execution_count": null,
 121    "metadata": {
 122     "collapsed": false
 123    },
 124    "outputs": [],
 125    "source": [
 126     "par(mfrow=c(1,2))\n",
 127     "f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste(\"Filaments nn exo day\",i_np))\n",
 128     "f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste(\"Filaments nn exo day\",i_p))\n",
 129     "\n",
 130     "f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste(\"Filaments nn mix day\",i_np))\n",
 131     "f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste(\"Filaments nn mix day\",i_p))"
 132    ]
 133   },
 134   {
 135    "cell_type": "code",
 136    "execution_count": null,
 137    "metadata": {
 138     "collapsed": false
 139    },
 140    "outputs": [],
 141    "source": [
 142     "par(mfrow=c(1,2))\n",
 143     "plotFilamentsBox(data, f_np_exo); title(paste(\"FilBox nn exo day\",i_np))\n",
 144     "plotFilamentsBox(data, f_p_exo); title(paste(\"FilBox nn exo day\",i_p))\n",
 145     "\n",
 146     "plotFilamentsBox(data, f_np_mix); title(paste(\"FilBox nn mix day\",i_np))\n",
 147     "plotFilamentsBox(data, f_p_mix); title(paste(\"FilBox nn mix day\",i_p))"
 148    ]
 149   },
 150   {
 151    "cell_type": "code",
 152    "execution_count": null,
 153    "metadata": {
 154     "collapsed": false
 155    },
 156    "outputs": [],
 157    "source": [
 158     "par(mfrow=c(1,2))\n",
 159     "plotRelVar(data, f_np_exo); title(paste(\"StdDev nn exo day\",i_np))\n",
 160     "plotRelVar(data, f_p_exo); title(paste(\"StdDev nn exo day\",i_p))\n",
 161     "\n",
 162     "plotRelVar(data, f_np_mix); title(paste(\"StdDev nn mix day\",i_np))\n",
 163     "plotRelVar(data, f_p_mix); title(paste(\"StdDev nn mix day\",i_p))\n",
 164     "\n",
 165     "# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir"
 166    ]
 167   },
 168   {
 169    "cell_type": "code",
 170    "execution_count": null,
 171    "metadata": {
 172     "collapsed": false
 173    },
 174    "outputs": [],
 175    "source": [
 176     "par(mfrow=c(1,2))\n",
 177     "plotSimils(p_nn_exo, i_np); title(paste(\"Weights nn exo day\",i_np))\n",
 178     "plotSimils(p_nn_exo, i_p); title(paste(\"Weights nn exo day\",i_p))\n",
 179     "\n",
 180     "plotSimils(p_nn_mix, i_np); title(paste(\"Weights nn mix day\",i_np))\n",
 181     "plotSimils(p_nn_mix, i_p); title(paste(\"Weights nn mix day\",i_p))\n",
 182     "\n",
 183     "# - pollué à gauche, + pollué à droite"
 184    ]
 185   },
 186   {
 187    "cell_type": "code",
 188    "execution_count": null,
 189    "metadata": {
 190     "collapsed": false
 191    },
 192    "outputs": [],
 193    "source": [
 194     "# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
 195     "p_nn_exo$getParams(i_np)$window\n",
 196     "p_nn_exo$getParams(i_p)$window\n",
 197     "\n",
 198     "p_nn_mix$getParams(i_np)$window\n",
 199     "p_nn_mix$getParams(i_p)$window"
 200    ]
 201   },
 202   {
 203    "cell_type": "markdown",
 204    "metadata": {},
 205    "source": [
 206     "\n",
 207     "\n",
 208     "<h2 style=\"color:blue;font-size:2em\">Pollution par épandage</h2>"
 209    ]
 210   },
 211   {
 212    "cell_type": "code",
 213    "execution_count": null,
 214    "metadata": {
 215     "collapsed": false
 216    },
 217    "outputs": [],
 218    "source": [
 219     "p_nn_exo = computeForecast(data, indices_ep, \"Neighbors\", \"Neighbors\", simtype=\"exo\", horizon=H)\n",
 220     "p_nn_mix = computeForecast(data, indices_ep, \"Neighbors\", \"Neighbors\", simtype=\"mix\", horizon=H)\n",
 221     "p_az = computeForecast(data, indices_ep, \"Average\", \"Zero\", horizon=H) #, memory=183)\n",
 222     "p_pz = computeForecast(data, indices_ep, \"Persistence\", \"Zero\", horizon=H, same_day=TRUE)"
 223    ]
 224   },
 225   {
 226    "cell_type": "code",
 227    "execution_count": null,
 228    "metadata": {
 229     "collapsed": false
 230    },
 231    "outputs": [],
 232    "source": [
 233     "e_nn_exo = computeError(data, p_nn_exo, H)\n",
 234     "e_nn_mix = computeError(data, p_nn_mix, H)\n",
 235     "e_az = computeError(data, p_az, H)\n",
 236     "e_pz = computeError(data, p_pz, H)\n",
 237     "options(repr.plot.width=9, repr.plot.height=7)\n",
 238     "plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4))\n",
 239     "\n",
 240     "# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence\n",
 241     "\n",
 242     "i_np = which.min(e_nn_exo$abs$indices)\n",
 243     "i_p = which.max(e_nn_exo$abs$indices)"
 244    ]
 245   },
 246   {
 247    "cell_type": "code",
 248    "execution_count": null,
 249    "metadata": {
 250     "collapsed": false
 251    },
 252    "outputs": [],
 253    "source": [
 254     "options(repr.plot.width=9, repr.plot.height=4)\n",
 255     "par(mfrow=c(1,2))\n",
 256     "\n",
 257     "plotPredReal(data, p_nn_exo, i_np); title(paste(\"PredReal nn exo day\",i_np))\n",
 258     "plotPredReal(data, p_nn_exo, i_p); title(paste(\"PredReal nn exo day\",i_p))\n",
 259     "\n",
 260     "plotPredReal(data, p_nn_mix, i_np); title(paste(\"PredReal nn mix day\",i_np))\n",
 261     "plotPredReal(data, p_nn_mix, i_p); title(paste(\"PredReal nn mix day\",i_p))\n",
 262     "\n",
 263     "plotPredReal(data, p_az, i_np); title(paste(\"PredReal az day\",i_np))\n",
 264     "plotPredReal(data, p_az, i_p); title(paste(\"PredReal az day\",i_p))\n",
 265     "\n",
 266     "# Bleu: prévue, noir: réalisée"
 267    ]
 268   },
 269   {
 270    "cell_type": "code",
 271    "execution_count": null,
 272    "metadata": {
 273     "collapsed": false
 274    },
 275    "outputs": [],
 276    "source": [
 277     "par(mfrow=c(1,2))\n",
 278     "f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste(\"Filaments nn exo day\",i_np))\n",
 279     "f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste(\"Filaments nn exo day\",i_p))\n",
 280     "\n",
 281     "f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste(\"Filaments nn mix day\",i_np))\n",
 282     "f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste(\"Filaments nn mix day\",i_p))"
 283    ]
 284   },
 285   {
 286    "cell_type": "code",
 287    "execution_count": null,
 288    "metadata": {
 289     "collapsed": false
 290    },
 291    "outputs": [],
 292    "source": [
 293     "par(mfrow=c(1,2))\n",
 294     "plotFilamentsBox(data, f_np_exo); title(paste(\"FilBox nn exo day\",i_np))\n",
 295     "plotFilamentsBox(data, f_p_exo); title(paste(\"FilBox nn exo day\",i_p))\n",
 296     "\n",
 297     "plotFilamentsBox(data, f_np_mix); title(paste(\"FilBox nn mix day\",i_np))\n",
 298     "plotFilamentsBox(data, f_p_mix); title(paste(\"FilBox nn mix day\",i_p))"
 299    ]
 300   },
 301   {
 302    "cell_type": "code",
 303    "execution_count": null,
 304    "metadata": {
 305     "collapsed": false
 306    },
 307    "outputs": [],
 308    "source": [
 309     "par(mfrow=c(1,2))\n",
 310     "plotRelVar(data, f_np_exo); title(paste(\"StdDev nn exo day\",i_np))\n",
 311     "plotRelVar(data, f_p_exo); title(paste(\"StdDev nn exo day\",i_p))\n",
 312     "\n",
 313     "plotRelVar(data, f_np_mix); title(paste(\"StdDev nn mix day\",i_np))\n",
 314     "plotRelVar(data, f_p_mix); title(paste(\"StdDev nn mix day\",i_p))\n",
 315     "\n",
 316     "# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir"
 317    ]
 318   },
 319   {
 320    "cell_type": "code",
 321    "execution_count": null,
 322    "metadata": {
 323     "collapsed": false
 324    },
 325    "outputs": [],
 326    "source": [
 327     "par(mfrow=c(1,2))\n",
 328     "plotSimils(p_nn_exo, i_np); title(paste(\"Weights nn exo day\",i_np))\n",
 329     "plotSimils(p_nn_exo, i_p); title(paste(\"Weights nn exo day\",i_p))\n",
 330     "\n",
 331     "plotSimils(p_nn_mix, i_np); title(paste(\"Weights nn mix day\",i_np))\n",
 332     "plotSimils(p_nn_mix, i_p); title(paste(\"Weights nn mix day\",i_p))\n",
 333     "\n",
 334     "# - pollué à gauche, + pollué à droite"
 335    ]
 336   },
 337   {
 338    "cell_type": "code",
 339    "execution_count": null,
 340    "metadata": {
 341     "collapsed": false
 342    },
 343    "outputs": [],
 344    "source": [
 345     "# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
 346     "p_nn_exo$getParams(i_np)$window\n",
 347     "p_nn_exo$getParams(i_p)$window\n",
 348     "\n",
 349     "p_nn_mix$getParams(i_np)$window\n",
 350     "p_nn_mix$getParams(i_p)$window"
 351    ]
 352   },
 353   {
 354    "cell_type": "markdown",
 355    "metadata": {},
 356    "source": [
 357     "\n",
 358     "\n",
 359     "<h2 style=\"color:blue;font-size:2em\">Semaine non polluée</h2>"
 360    ]
 361   },
 362   {
 363    "cell_type": "code",
 364    "execution_count": null,
 365    "metadata": {
 366     "collapsed": false
 367    },
 368    "outputs": [],
 369    "source": [
 370     "p_nn_exo = computeForecast(data, indices_np, \"Neighbors\", \"Neighbors\", simtype=\"exo\", horizon=H)\n",
 371     "p_nn_mix = computeForecast(data, indices_np, \"Neighbors\", \"Neighbors\", simtype=\"mix\", horizon=H)\n",
 372     "p_az = computeForecast(data, indices_np, \"Average\", \"Zero\", horizon=H) #, memory=183)\n",
 373     "p_pz = computeForecast(data, indices_np, \"Persistence\", \"Zero\", horizon=H, same_day=FALSE)"
 374    ]
 375   },
 376   {
 377    "cell_type": "code",
 378    "execution_count": null,
 379    "metadata": {
 380     "collapsed": false
 381    },
 382    "outputs": [],
 383    "source": [
 384     "e_nn_exo = computeError(data, p_nn_exo, H)\n",
 385     "e_nn_mix = computeError(data, p_nn_mix, H)\n",
 386     "e_az = computeError(data, p_az, H)\n",
 387     "e_pz = computeError(data, p_pz, H)\n",
 388     "options(repr.plot.width=9, repr.plot.height=7)\n",
 389     "plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4))\n",
 390     "\n",
 391     "# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence\n",
 392     "\n",
 393     "i_np = which.min(e_nn_exo$abs$indices)\n",
 394     "i_p = which.max(e_nn_exo$abs$indices)"
 395    ]
 396   },
 397   {
 398    "cell_type": "code",
 399    "execution_count": null,
 400    "metadata": {
 401     "collapsed": false
 402    },
 403    "outputs": [],
 404    "source": [
 405     "options(repr.plot.width=9, repr.plot.height=4)\n",
 406     "par(mfrow=c(1,2))\n",
 407     "\n",
 408     "plotPredReal(data, p_nn_exo, i_np); title(paste(\"PredReal nn exo day\",i_np))\n",
 409     "plotPredReal(data, p_nn_exo, i_p); title(paste(\"PredReal nn exo day\",i_p))\n",
 410     "\n",
 411     "plotPredReal(data, p_nn_mix, i_np); title(paste(\"PredReal nn mix day\",i_np))\n",
 412     "plotPredReal(data, p_nn_mix, i_p); title(paste(\"PredReal nn mix day\",i_p))\n",
 413     "\n",
 414     "plotPredReal(data, p_az, i_np); title(paste(\"PredReal az day\",i_np))\n",
 415     "plotPredReal(data, p_az, i_p); title(paste(\"PredReal az day\",i_p))\n",
 416     "\n",
 417     "# Bleu: prévue, noir: réalisée"
 418    ]
 419   },
 420   {
 421    "cell_type": "code",
 422    "execution_count": null,
 423    "metadata": {
 424     "collapsed": false
 425    },
 426    "outputs": [],
 427    "source": [
 428     "par(mfrow=c(1,2))\n",
 429     "f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste(\"Filaments nn exo day\",i_np))\n",
 430     "f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste(\"Filaments nn exo day\",i_p))\n",
 431     "\n",
 432     "f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste(\"Filaments nn mix day\",i_np))\n",
 433     "f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste(\"Filaments nn mix day\",i_p))"
 434    ]
 435   },
 436   {
 437    "cell_type": "code",
 438    "execution_count": null,
 439    "metadata": {
 440     "collapsed": false
 441    },
 442    "outputs": [],
 443    "source": [
 444     "par(mfrow=c(1,2))\n",
 445     "plotFilamentsBox(data, f_np_exo); title(paste(\"FilBox nn exo day\",i_np))\n",
 446     "plotFilamentsBox(data, f_p_exo); title(paste(\"FilBox nn exo day\",i_p))\n",
 447     "\n",
 448     "plotFilamentsBox(data, f_np_mix); title(paste(\"FilBox nn mix day\",i_np))\n",
 449     "plotFilamentsBox(data, f_p_mix); title(paste(\"FilBox nn mix day\",i_p))"
 450    ]
 451   },
 452   {
 453    "cell_type": "code",
 454    "execution_count": null,
 455    "metadata": {
 456     "collapsed": false
 457    },
 458    "outputs": [],
 459    "source": [
 460     "par(mfrow=c(1,2))\n",
 461     "plotRelVar(data, f_np_exo); title(paste(\"StdDev nn exo day\",i_np))\n",
 462     "plotRelVar(data, f_p_exo); title(paste(\"StdDev nn exo day\",i_p))\n",
 463     "\n",
 464     "plotRelVar(data, f_np_mix); title(paste(\"StdDev nn mix day\",i_np))\n",
 465     "plotRelVar(data, f_p_mix); title(paste(\"StdDev nn mix day\",i_p))\n",
 466     "\n",
 467     "# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir"
 468    ]
 469   },
 470   {
 471    "cell_type": "code",
 472    "execution_count": null,
 473    "metadata": {
 474     "collapsed": false
 475    },
 476    "outputs": [],
 477    "source": [
 478     "par(mfrow=c(1,2))\n",
 479     "plotSimils(p_nn_exo, i_np); title(paste(\"Weights nn exo day\",i_np))\n",
 480     "plotSimils(p_nn_exo, i_p); title(paste(\"Weights nn exo day\",i_p))\n",
 481     "\n",
 482     "plotSimils(p_nn_mix, i_np); title(paste(\"Weights nn mix day\",i_np))\n",
 483     "plotSimils(p_nn_mix, i_p); title(paste(\"Weights nn mix day\",i_p))\n",
 484     "\n",
 485     "# - pollué à gauche, + pollué à droite"
 486    ]
 487   },
 488   {
 489    "cell_type": "code",
 490    "execution_count": null,
 491    "metadata": {
 492     "collapsed": false
 493    },
 494    "outputs": [],
 495    "source": [
 496     "# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
 497     "p_nn_exo$getParams(i_np)$window\n",
 498     "p_nn_exo$getParams(i_p)$window\n",
 499     "\n",
 500     "p_nn_mix$getParams(i_np)$window\n",
 501     "p_nn_mix$getParams(i_p)$window"
 502    ]
 503   },
 504   {
 505    "cell_type": "markdown",
 506    "metadata": {},
 507    "source": [
 508     "\n",
 509     "\n",
 510     "<h2>Bilan</h2>\n",
 511     "\n",
 512     "Problème difficile : on ne fait guère mieux qu'une naïve moyenne des lendemains des jours\n",
 513     "similaires dans le passé, ce qui n'est pas loin de prédire une série constante égale à la\n",
 514     "dernière valeur observée (méthode \"zéro\"). La persistence donne parfois de bons résultats\n",
 515     "mais est trop instable (sensibilité à l'argument <code>same_day</code>).\n",
 516     "\n",
 517     "Comment améliorer la méthode ?"
 518    ]
 519   }
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